And to perform the ZSL task, the dataset needs to be divided into two disjoint subsets. In order to reflect the effectiveness of the method, we separately perform Standard Split (SS) and Propose Split (PS) on the dataset. PS was proposed by literature[1]}. Since most zero-shot learning methods use the features extracted by the pre-trained network model trained by ImageNet, and the standard cut of the zero-shot dataset has unseen class that overlap with some of the classes in ImageNet. As a result, the ZSL method has higher accuracy in overlapping classes, so a new Split scheme is proposed—Propose Split.
